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홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

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Node Classification On Amazon Photo 1

평가 지표

Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	Accuracy	Paper Title	Repository
CoLinkDist	94.36%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	-
GraphSAGE	96.78 ± 0.23	Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification	-
CoLinkDistMLP	94.12%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	-
GNNMoE(GAT-like P)	95.71±0.37	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification	-
GCN	96.10 ± 0.46	Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification	-
GAT	96.60 ± 0.33	Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification	-
3ference	95.05%	Inferring from References with Differences for Semi-Supervised Node Classification on Graphs
LinkDist	93.75%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	-
GNNMoE(GCN-like P)	95.81±0.41	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification	-
GNNMoE(SAGE-like P)	95.46±0.24	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification	-
LinkDistMLP	93.83%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	-

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Node Classification On Amazon Photo 1 | SOTA | HyperAI초신경